evaluate-falsifiability

SkillDev tools

Lets your agent test whether a hypothesis can be disproven and spot claims that can't be.

Available today. Use it from your connected AI after setup.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the evaluate-falsifiability skill

About this skill

Determine what observation would falsify a hypothesis and flag unfalsifiable formulations.

What this skill tells your AI

The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/evaluate-falsifiability/SKILL.md and read by ahel’s review.

Purpose

Evaluate whether a claim or hypothesis exposes observations that could count against it.

Input contract

required: [claim, prediction_set, observation_domain]
optional: [auxiliary_assumptions, measurement_limits]
constraints: [falsifying conditions must be observable within the declared domain]

Procedure

  1. Translate the claim into testable predictions and boundary conditions.
  2. Identify observations that would contradict the claim under its assumptions.
  3. Check whether those observations are measurable and independent of the claim's definition.
  4. Classify falsifiability and list needed operationalization.

Output contract

produces: [falsifiability_assessment, falsifying_observations, operationalization_gaps, assumption_dependencies]
delta_fields: [findings, hypothesis_updates, uncertainties, open_questions]

Quality gates

  • At least one non-vacuous potential counter-observation is explicit for a falsifiable claim.
  • Auxiliary assumptions are separated from the core claim.

Failure and counterexamples

Do not call a claim falsifiable merely because it can be criticized rhetorically.

Provenance map

  • resolved: evaluate-falsifiability

Signals

GitHub stars
501
Forks
41
Last commit
Sep 2026
Advanced
Catalog kind
skill
Key
evaluate-falsifiability
Source
github.com/yogsoth-ai/de-anthropocentric-research-engine